Выявление аномалии при прогнозном анализе данных

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В.И. Кузовлев, А.О. Орлов
84 ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2016. № 5
Просьба ссылаться на эту статью следующим образом:
Кузовлев В.И., Орлов А.О. Выявление аномалий при прогнозном анализе данных //
Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2016. № 5. C. 75–85.
DOI: 10.18698/0236-3933-2016-5-75-85
ANOMALIES DETECTION IN PROGNOSTIC DATA ANALYSIS
V.I. Kuzovlev
A.O. Orlov
Bauman Moscow State Technical University, Moscow, Russian Federation
Abstract Keywords
Designing data models for prognostic purposes require
anomalies detection method. This article describes the
choice of the method and how it applies for the decision
tree model algorithm. The authors not only describe the
methods of data anomalies search, but also explain basic
steps of the algorithm itself. The work analyzes search pa-
rameters and their major influence on the method applica-
tion outcome. As a result of both anomalies detection
methods and decision tree model algorithm design the
accuracy of the prognostic model increases. It happens due
to improved model robustness and also a significant per-
formance improvement of the analysis
Anomalies, outliers in data, prog-
nostic analysis, decision tree model
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